Association of Recent SARS-CoV-2 Infection With New-Onset Alcohol Use Disorder, January 2020 Through January 2022

Key Points Question Did the risk of a new alcohol use disorder (AUD) diagnosis after COVID-19 infection change over time during the pandemic? Findings A cohort study using electronic health records of 2 821 182 US patients compared the risk of developing AUD after COVID-19 with that among patients after non–COVID-19 respiratory infections. An excess risk of a new diagnosis of AUD with COVID-19 was observed in the beginning of the pandemic, which then subsided, increased again for infections contracted from January to July 2021, and then became nonsignificant again after August 2021. Meaning The results of this study suggest that the risk of a new diagnosis of AUD after a COVID-19 diagnosis may not be a consequence of the infection itself but rather associated with the context of the diagnosis and the pandemic.


95209-3: SARS coronavirus+SARS coronavirus 2 Ag
in Respiratory specimen by Rapid immunoassay (labResult: Positive) 94763-0: SARS-CoV-2   [Presence] in Unspecified specimen by Organism specific culture (labResult: Positive) 96603-6: SARS-CoV-2 (COVID-19) S protein RBD neutralizing antibody [Presence] in Serum or Plasma by Immunoassay (labResult: Positive) 96119-3: SARS-CoV-2 (COVID-19) Ag [Presence] in Upper respiratory specimen by Immunoassay (labResult: Positive) For both COVID-19 and control cohorts, patients were excluded if they had ever been diagnosed with an F10 code (alcohol related disorders) prior to their index event. Lastly, patients in all cohorts (both COVID-19 and controls) were excluded if they had died up until 2 weeks after the last day of the index event window. This ensured that only alive patients were included in the analyses up until the follow-up window for outcomes began, which was two weeks after the index event for patients, even if a patient had the index event on the very last day of their respective index event time window.

Details on statistical analyses
The status of SARS-CoV-2 infection was based on the International Classification of Diseases (ICD-10) diagnosis of U07.1. The outcome measure of alcohol use disorder was determined by the presence of a new diagnosis of an alcohol-related disorder (F10). SARS-CoV-2 infections were recorded for patients aged 12 years of age and older. Each COVID-19 cohort was matched with a cohort of patients without SARS-CoV2 infection by the TriNetX built-in propensity score matching function (1:1 matching using a nearest neighbor greedy matching algorithm with a caliper of 0.25 times the standard deviation). Risk of new AUD diagnosis following date of infection was then compared for the SARS-CoV2 cohort to the other respiratory infection cohort using hazard ratios and 95% confidence intervals. Kaplan-Meier analysis was used to estimate the probability of clinical outcomes. Cox's proportional hazards model was used to compare the two matched cohorts. The proportional hazard assumption was tested using the generalized Schoenfeld approach. In our main analyses, there was a single violation of proportionality in block 5 for 3-6 months of follow-up with a p of 0.0048. The TriNetX Platform calculates the hazard ratios and associated confidence intervals, using R's Survival package v3.2-3. For generating hazard ratios, TriNetX sets robust=FALSE using the R survival package, but it does not take into account potential clustering of COVID-19 cases within the healthcare organizations or specific geolocations, a potential weakness or confounding factor in the analysis.
Additional cohorts were run to test the robustness of the findings presented. For one, COVID-19 cohorts were defined differently to include positive COVID-19 tests (using rollup lab code 9088 for any positive RNA test ever) in addition to the option of a COVID-19 diagnosis (U07.1). Blocks four and eight of these analyses had to be split into two smaller time blocks because the additions of the 9088 code for inclusion made these cohorts too large to be run with the original index event timeframes. Block 4 was originally set for index events that occurred from 10/23/20-1/23/20. To reduce the cohort size, two cohorts were made to capture this time frame. Block 4 Part 1 indexes diagnoses from 10/23/20 and 12/04/20. Block 4 Part 2 indexes diagnoses from 12/05/21 to 1/23/21. Block 8 was originally set for index events that occurred from 10/27/21-10/27/22. To reduce the cohort size, two cohorts were made to capture this time frame. Block 8 Part 1 indexes diagnoses from 10/27/21 to 12/8/21. Block 8 Part 2 indexes diagnoses from 12/09/21-1/27/22. These results are displayed in figures 1 and 2. These analyses gave a similar pattern of HRs compared to the main analyses that only used U07.1, but they were slightly lower than those for the U07.1 cohorts only.
Another set of analyses was run by changing the control cohorts to the index event of large bone fractures (adapted from Taquet et al.) rather than other respiratory infections. The codes used for inclusion were any of the following: S32: Fracture of lumbar spine and pelvis S42: Fracture of shoulder and upper arm S52: Fracture of forearm F72: Fracture of femur S82: Fracture of lower leg, including ankle These analyses also yielded similar temporal results to those presented in the analyses and are shown in figures 3 and 4.
Lastly, cohorts for COVID-19 vs ORI were run in delta and omicron specific time periods to see if these waves of COVID-19 showed any break from the HR trend observed in the three-month blocks of time that we originally ran. These results are presented in figures 5 and 6. These results showed no difference in risk of first diagnosis of an alcohol-related disorder. Table 1 in the article presented baseline characteristics for the block 1 cohorts. This table was additionally reduced to only show demographics, risk factors, and common comorbidities for addictive disease. An eFigure 1. This plot shows the HRs and 95% CIs when COVID-19 groups that had the additional inclusion criteria of a positive COVID-19 test were compared to the same ORI cohorts used in the main analysis. The follow-up period for these results is from 14 days to 3 months after the index event, which is a diagnosis or positive test of COVID-19 for the exposure group and diagnosis of another respiratory infection for the control group. eFigure 2. This plot shows the HRs and 95% CIs when COVID-19 groups that had the additional inclusion criteria of a positive COVID-19 test were compared to the same ORI cohorts used in the main analysis. The follow-up period for these results is from 3 to 6 months after the index event, which is a diagnosis or positive test of COVID-19 for the exposure group and diagnosis of another respiratory infection for the control group.

Characteristics for all cohorts
eFigure 3. This plot shows the HRs and 95% CIs when COVID-19 were compared to control cohorts defined by a large bone fracture (adapted from Taquet et al.). The follow-up period for these results is from 14 days to 3 months after the index event, which is a diagnosis of COVID-19 for the exposure group and diagnosis of another respiratory infection for the control group.
eFigure 4. This plot shows the HRs and 95% CIs when COVID-19 were compared to control cohorts defined by a large bone fracture (adapted from Taquet et al.). The followup period for these results is from 3 to 6 months after the index event, which is a diagnosis of COVID-19 for the exposure group and diagnosis of another respiratory infection for the control group.